Increasingly, multiple studies relating biomarkers to cancer and other health outcomes are pooled to obtain an overall risk profile, and a major challenge of pooling biomarker data is potential sources of variability of the biomarker data, including assay and laboratory variability. Currently there are no reliable and well-evaluated statistical methods to conduct the aggregated analysis for pooled biomarker data while taking care of the calibration process that correct for the between-study biomarker variability. In this proposal, we will develop efficient statistical methods for incorporating the calibration process in the aggregated data analysis. User- friendly software implementing the methods will be made publicly available. In addition, analysis results have potential to be substantially different between using the two commonly used methods for analyzing pooled data, the two-stage analysis method and the aggregated data analysis method, and in the two-stage method, between the fixed effect model method and the random effect model method. Investigators conducting consortial research are confronted with the choice between the methods. We will compare these methods such that the choices of analysis methods will be made to exploit the full power of the data available to maximize the information gained, while at the same time only making minimum and realistic assumptions.